Security systems are finding an ever increasing usage in monitoring installations. Such systems can range from one or two cameras in a small location (e.g., a store) or up to hundreds or thousands of cameras covering a large mall, building, airport, military installation and so forth. In general, such systems display video signals as discrete individual pictures on a number of display panels. When there are a large number of cameras, greater than the number of display panels, the systems can possess a control means that changes the input signal to the displays so as to rotate the images and scan the entire video coverage within a predetermined time frame. Such systems also usually have mechanisms for stopping the progression of the image sequence to allow for the study of a particular area of interest. Such systems have proved useful in monitoring areas and frequently result in the identification of criminal activity.
The use of video cameras in security and surveillance systems typically involves some form of video image processing, including a detection of a region of interest (ROI) within a field of view of an imaging camera (e.g., video camera). Detecting an ROI in images is a common feature of many image processing software applications. Conventional digital image recognition software routines, for example, are capable of detecting an ROI. Generally, an image is composed of many objects that can be defined by pixels. A group of pixels can be referred to as a “region”. A group of pixels that belongs to a specific object that an operator is interested in is generally referred to as a “region of interest”. A target can be referred to as an “object of interest”. Some examples of regions of interest and targets include, for example, human, vehicles and faces. In one example, a prototype may contain information about a region of interest as related to a type of target. An image-processing component may therefore detect a region in an image that matches the prototype.
Algorithmic video image processing software applications, as required by many security and surveillance application, typically detect the presence of a target in video images. The movement of the target may further be monitored via tracking. Conventional approaches typically detect the target using segmentation, feature extraction and classification processes. The segmentation process, which can be edge-based or region growing based, extracts the outlines of the objects in the image. Feature extraction computes the discriminatory characteristics of the extracted segment. Finally, the classification process evaluates the features and determines whether the segmented object is a target of interest.
Some prior art video image processing systems involve the detection of an abandoned object. Such image processing systems monitor the background of the image area. Tracking of the movement of people, along with blob-based object detection and comparison with a known background can determine the presence or absence of an abandoned object. In such applications, the image processing system detects an additional object in the background. Note that the background that is free of the abandoned object can be acquired from previous frames.
Some security and surveillance applications may transmit an alert when a high value object, such as a painting, jewelry, or an antique is removed from a display or a site. One detecting device is a video camera, which constantly captures the images of the object and determines if the object is in the images or not. In such applications, the target of interest is known, while the background, which the target shields, may not be known.
Some prior art image processing technique often apply change detection approaches. Such techniques or systems typically establish a reference background (e.g., target-reference image), part of which contains the target of interest. Subsequent images can be compared with this target-reference image to determine whether or note the target remains in the images. Change detection is not robust, particularly with respect to lighting changes or shadows. Some prior art image processing techniques improve change detection capabilities using learning or adaptation, such as adaptive thresholds. One serious problem with using change detection approaches for such an application is the risk of false alarms due to occlusion. When a viewer partially occludes the monitored object from the camera's field of view; the change-detection-based monitoring system detects a significant change and the disappearance of the target of interest. Thus, such a system often generates an alarm.
It is believed that a need therefore exists for an improved image processing video imaging and surveillance method and system that overcomes these problems. Such an improved, robust, removed-object-detection method and system that is immune to environmental changes and occlusion is described in greater detail herein.